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Description

Overview:

This 2-day seminar provides a 1-day introduction to the statistical tools used to analyze Design Verification data and Process Validation results. The entire 2nd day is spent on Statistical Process Control and Process Capability Indices. The goal of the 1st day is to help the student understand how to choose statistical methods and sample sizes, and to correctly interpret the results. The goal of the 2nd say is to explain how to monitor a validated production process, using tools that can also help improve product quality.

Why should you attend?

All design and/or manufacturing companies perform design verification and/or process validation studies. A clear understanding of relevant statistical principles and statistical methods ensures that such studies are efficient and accurate. In addition, all validated processes must be monitored to ensure their continued suitability (per the FDA).

The statistical methods used for such activities are easily misused when their fundamental principles are not well understood. Mistakes in usage can lead to new products being launched that should have been kept in R&D or, conversely, can lead to erroneously deciding to not launch a new product. And failure to monitor production processes accurately can lead to a slow decline in product quality.

This seminar provides a thorough, practical introduction to the relevant statistical methods and principles that will help ensure that outputs from R&D, Product Transfer, Manufacturing Engineering, and Production are consistently of high quality.

Areas Covered in the Session:

FDA, ISO 9001/13485, and MDD requirements

Statistically valid rationales for sample sizes

The interpretation of statistical significance and statistical non-significance

Lecture 5:How to perform and interpret simple t-Tests of Statistical Significance, including consideration of "p-values" and sample-size, and the concepts of "superiority" and "non-inferiority".

Lecture 6:Calculation of confidence and reliability (= % in-specification) for

attribute data

normally-distributed variables data (including Tests of Normality)

non-normal data (including Transformations to Normality)

non-normal data that cannot be transformed to normality

Day 2 Schedule:

STATISTICAL PROCESS CONTROL (SPC) AND PROCESS CAPABILITY INDICES

Lecture 1:What is Quality?

Lecture 2:Process Variation

Lecture 3:What is Statistical Process Control?

Lecture 4:Basic Types of Control Charts and how to construct them: XbarR, XbarS, XmR, P, and U.

Lecture 5:Control Limits: Calculation & Re-calculation

Lecture 6:Out of Control: How to Detect It, & What to Do if Detect It?

Lecture 7:Sample Issues: Random, Sub-grouping, & Sample Size

Lecture 8:Capability Indices and how to calculation them

Lecture 9:Non-normal Data, and its impact on SPC.

Lecture 10:How to Initiate & Implement a Successful SPC Program

Speaker:

John Zorich

Statistical Consultant & Trainer, Ohlone College & SV Polytechnic

John N. Zorich,has spent 35 years in the medical device manufacturing industry; the first 20 years were as a "regular" employee in the areas of R&D, Manufacturing, QA/QC, and Regulatory; the last 15 years were as consultant in the areas of QA/QC and Statistics. His consulting clients in the area of statistics have included numerous start-ups as well as large corporations such as Boston Scientific, Novellus, and Siemens Medical. His experience as an instructor in statistics includes having given 3-day workshop/seminars for the past several years at Ohlone College (San Jose CA), 1-day training workshops in SPC for Silicon Valley Polytechnic Institute (San Jose CA) for several years, several 3-day courses for ASQ Biomedical, numerous seminars at ASQ meetings and conferences, and half-day seminars for numerous private clients. He creates and sells formally-validated statistical application spreadsheets that have been purchased by more than 75 companies, world-wide.